Learning robust task priorities of QP-based whole-body torque-controllers

Generating complex whole-body movements for humanoid robots is now most often achieved with multi-task whole-body controllers based on quadratic programming. To perform on the real robot, such controllers often require a human expert to tune or optimize the many parameters of the controller related to the tasks and to the specific robot, which is generally reported as a tedious and time consuming procedure. This problem can be tackled by automatically optimizing some parameters such as task priorities or task trajectories, while ensuring constraints satisfaction, through simulation. However, this does not guarantee that parameters optimized in simulation will also be optimal for the real robot. As a solution, the present paper focuses on optimizing task priorities in a robust way, by looking for solutions which achieve desired tasks under a variety of conditions and perturbations. This approach, which can be referred to as domain randomization, can greatly facilitate the transfer of optimized solutions from simulation to a real robot. The proposed method is demonstrated using a simulation of the humanoid robot iCub for a whole-body stepping task.